scholarly journals Study on Icing Environment Judgment Based on Radar Data

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1534
Author(s):  
Jinhu Wang ◽  
Binze Xie ◽  
Jiahan Cai ◽  
Yuhao Wang ◽  
Jiang Chen
Keyword(s):  
Neural Network ◽  
Principal Component ◽  
Spectral Width ◽  
Radar Data ◽  
Lattice Points ◽  
Civil Aviation ◽  
Quantitative Calculation ◽  
Ice Accumulation ◽  
Wave Radar ◽  
Accumulation Index

As a major threat to aviation flight safety, it is particularly important to make accurate judgments and forecasts of the ice accumulation environment. Radar is widely used in civil aviation and meteorology, and has the advantages of high timeliness and resolution. In this paper, a variety of machine learning methods are used to establish the relationship between radar data and icing index (Ic) to determine the ice accumulation environment. The research shows the following. (1) A linear model was established, based on the scattering rate factor (Zh), radial velocity (v), spectral width (w), velocity standard deviation (σ) detected by 94 GHz millimeter wave radar, and backward attenuation coefficient (β) detected by 905 nm lidar, so linear regression was carried out. After principal component analysis (PCA), the correction determination coefficient of the linear equation was increased from 0.7127 to 0.7240. (2) Ice accumulation was unlikely for samples that were significantly off-center. By clustering the data into three or four categories, the proportion of icing lattice points could be increased from 18.81% to 33.03%. If the clustering number was further increased, the ice accumulation ratio will not be further increased, and the increased classification is reflected in the classification of pairs of noises and the possibility of omission is also increased. (3) Considering the classification and nonlinear factors of ice accumulation risk, the neural network method was used to judge the ice accumulation environment. Two kinds of neural network structures were established for quantitative calculation: Structure 1 first distinguished whether there was ice accumulation, and further calculated the icing index for the points where there was ice accumulation; Structure 2 directly calculated the temperature and relative humidity, and calculated the icing index according to definition. The accuracy of the above two structures could reach nearly 60%, but the quantitative judgment of the ice accumulation index was not ideal. The reasons for this dissatisfaction may be the small number of variables and samples, the interval between time and space, the difference in instrument detection principle, and the representativeness of the ice accumulation index. Further research can be improved from the above four points. This study can provide a theoretical basis for the diagnosis and analysis of the aircraft ice accumulation environment.

scholarly journals On the Effect of Training Convolution Neural Network for Millimeter-Wave Radar-Based Hand Gesture Recognition

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 259
Author(s):  
Kang Zhang ◽  
Shengchang Lan ◽  
Guiyuan Zhang
Keyword(s):  
Neural Network ◽  
Millimeter Wave ◽  
Gesture Recognition ◽  
Radar Data ◽  
Hand Gesture Recognition ◽  
Convolution Neural Network ◽  
Hand Gesture ◽  
Millimeter Wave Radar ◽  
Average Accuracy ◽  
Wave Radar

The purpose of this paper was to investigate the effect of a training state-of-the-art convolution neural network (CNN) for millimeter-wave radar-based hand gesture recognition (MR-HGR). Focusing on the small training dataset problem in MR-HGR, this paper first proposed to transfer the knowledge with the CNN models in computer vision to MR-HGR by fine-tuning the models with radar data samples. Meanwhile, for the different data modality in MR-HGR, a parameterized representation of temporal space-velocity (TSV) spectrogram was proposed as an integrated data modality of the time-evolving hand gesture features in the radar echo signals. The TSV spectrograms representing six common gestures in human–computer interaction (HCI) from nine volunteers were used as the data samples in the experiment. The evaluated models included ResNet with 50, 101, and 152 layers, DenseNet with 121, 161 and 169 layers, as well as light-weight MobileNet V2 and ShuffleNet V2, mostly proposed by many latest publications. In the experiment, not only self-testing (ST), but also more persuasive cross-testing (CT), were implemented to evaluate whether the fine-tuned models generalize to the radar data samples. The CT results show that the best fine-tuned models can reach to an average accuracy higher than 93% with a comparable ST average accuracy almost 100%. Moreover, in order to alleviate the problem caused by private gesture habits, an auxiliary test was performed by augmenting four shots of the gestures with the heaviest misclassifications into the training set. This enriching test is similar with the scenario that a tablet reacts to a new user. The results of two different volunteer in the enriching test shows that the average accuracy of the enriched gesture can be improved from 55.59% and 65.58% to 90.66% and 95.95% respectively. Compared with some baseline work in MR-HGR, the investigation by this paper can be beneficial in promoting MR-HGR in future industry applications and consumer electronic design.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

Introducing Asymmetry into Interneuron Learning

1995 ◽  
Vol 7 (6) ◽  
pp. 1191-1205 ◽  
Author(s):  
Colin Fyfe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Negative Feedback ◽  
Principal Components ◽  
Network Architecture ◽  
Hebbian Learning ◽  
Principal Component ◽  
Neural Network Architecture ◽  
Weight Decay ◽  
Artificial Neural Network Architecture

A review is given of a new artificial neural network architecture in which the weights converge to the principal component subspace. The weights learn by only simple Hebbian learning yet require no clipping, normalization or weight decay. The net self-organizes using negative feedback of activation from a set of "interneurons" to the input neurons. By allowing this negative feedback from the interneurons to act on other interneurons we can introduce the necessary asymmetry to cause convergence to the actual principal components. Simulations and analysis confirm such convergence.

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